Statistical Calibration Algorithms for Lidars

Abstract: Robots are becoming increasingly available and capable, are becoming part of everyday life in applications: robots that guide blind or mentally handicapped people, robots that clean large office buildings and department stores, robots that assist people in shopping, recreational activities, etc. Localization, in the sense of understanding accurately one's position in the environment, is a basic building block for performing important tasks. Therefore, there is an interest in having robots to perform autonomously and accurately localization tasks in highly cluttered and dynamically changing environments. To perform localization, robots are required to opportunely combine their sensors measurements, sensors models and environment model. In this thesis we aim at improving the tools that constitute the basis of all the localization techniques, that are the models of these sensors, and the algorithms for processing the raw information from them. More specifically we focus on: - finding advanced statistical models of the measurements returned by common laser scanners (a.k.a. Lidars), starting from both physical considerations and evidence collected with opportune experiments;- improving the statistical algorithms for treating the signals coming from these sensors, and thus propose new estimation and system identification techniques for these devices. In other words, we strive for increasing the accuracy of Lidars through opportune statistical processing tools. The problems that we have to solve, in order to achieve our aims, are multiple. The first one is related to temperature dependency effects: the laser diode characteristics, especially the wave length of the emitted laser and the mechanical alignment of the optics, change non-linearly with temperature. In one of the papers in this thesis we specifically address this problem and propose a model describing the effects of temperature changes in the laser diode; these include, among others, the presence of multi-modal measurement noises. Our contributions then include an algorithm that statistically accounts not only for the bias induced by temperature changes, but also for these multi-modality issues. An other problem that we seek to relieve is an economical one. Improving the Lidar accuracy can be achieved by using accurate but expensive laser diodes and optical lenses. This unfortunately raises the sensor cost, and -- obviously -- low cost robots should not be equipped with very expensive Lidars. On the other hand, cheap Lidars have larger biases and noise variance. In an other contribution we thus precisely targeted the problem of how to improve the performance indexes of inexpensive Lidars by removing their biases and artifacts through opportune statistical manipulations of the raw information coming from the sensor. To achieve this goal it is possible to choose two different ways (that have been both explored): 1- use the ground truth to estimate the Lidar model parameters;2- find algorithms that perform simultaneously calibration and estimation withoutusing ground truth information.  Using the ground truth is appealing since it may lead to better estimation performance. On the other hand, though, in normal robotic operations the actual ground truth is not available -- indeed ground truths usually require environmental modifications, that are costly. We thus considered how to estimate the Lidar model parameters for both the cases above. In last chapter of this thesis we conclude our findings and propose also our current future research directions.